The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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#include "test_precomp.hpp"
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#include <iostream>
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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TEST(Optim_LpSolver, regression_basic){
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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cv::Mat A,B,z,etalon_z;
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#if 1
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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//cormen's example #1
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A=(cv::Mat_<double>(3,1)<<3,1,2);
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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B=(cv::Mat_<double>(3,4)<<1,1,3,30,2,2,5,24,4,1,2,36);
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std::cout<<"here A goes\n"<<A<<"\n";
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cv::optim::solveLP(A,B,z);
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std::cout<<"here z goes\n"<<z<<"\n";
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etalon_z=(cv::Mat_<double>(3,1)<<8,4,0);
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
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#endif
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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#if 1
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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//cormen's example #2
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A=(cv::Mat_<double>(1,2)<<18,12.5);
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B=(cv::Mat_<double>(3,3)<<1,1,20,1,0,20,0,1,16);
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std::cout<<"here A goes\n"<<A<<"\n";
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cv::optim::solveLP(A,B,z);
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std::cout<<"here z goes\n"<<z<<"\n";
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etalon_z=(cv::Mat_<double>(2,1)<<20,0);
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
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#endif
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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#if 1
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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//cormen's example #3
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A=(cv::Mat_<double>(1,2)<<5,-3);
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B=(cv::Mat_<double>(2,3)<<1,-1,1,2,1,2);
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std::cout<<"here A goes\n"<<A<<"\n";
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cv::optim::solveLP(A,B,z);
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std::cout<<"here z goes\n"<<z<<"\n";
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etalon_z=(cv::Mat_<double>(2,1)<<1,0);
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
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#endif
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}
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TEST(Optim_LpSolver, regression_init_unfeasible){
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cv::Mat A,B,z,etalon_z;
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#if 1
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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//cormen's example #4 - unfeasible
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A=(cv::Mat_<double>(1,3)<<-1,-1,-1);
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B=(cv::Mat_<double>(2,4)<<-2,-7.5,-3,-10000,-20,-5,-10,-30000);
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std::cout<<"here A goes\n"<<A<<"\n";
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cv::optim::solveLP(A,B,z);
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std::cout<<"here z goes\n"<<z<<"\n";
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etalon_z=(cv::Mat_<double>(3,1)<<1250,1000,0);
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
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#endif
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
12 years ago
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}
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TEST(Optim_LpSolver, regression_absolutely_unfeasible){
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cv::Mat A,B,z,etalon_z;
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#if 1
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//trivial absolutely unfeasible example
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A=(cv::Mat_<double>(1,1)<<1);
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B=(cv::Mat_<double>(2,2)<<1,-1);
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std::cout<<"here A goes\n"<<A<<"\n";
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int res=cv::optim::solveLP(A,B,z);
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ASSERT_EQ(res,-1);
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#endif
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}
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TEST(Optim_LpSolver, regression_multiple_solutions){
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cv::Mat A,B,z,etalon_z;
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#if 1
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//trivial example with multiple solutions
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A=(cv::Mat_<double>(2,1)<<1,1);
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B=(cv::Mat_<double>(1,3)<<1,1,1);
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std::cout<<"here A goes\n"<<A<<"\n";
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int res=cv::optim::solveLP(A,B,z);
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printf("res=%d\n",res);
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printf("scalar %g\n",z.dot(A));
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std::cout<<"here z goes\n"<<z<<"\n";
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ASSERT_EQ(res,1);
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ASSERT_EQ(z.dot(A),1);
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#endif
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}
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TEST(Optim_LpSolver, regression_cycling){
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cv::Mat A,B,z,etalon_z;
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#if 1
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//example with cycling from http://people.orie.cornell.edu/miketodd/or630/SimplexCyclingExample.pdf
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A=(cv::Mat_<double>(4,1)<<10,-57,-9,-24);
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B=(cv::Mat_<double>(3,5)<<0.5,-5.5,-2.5,9,0,0.5,-1.5,-0.5,1,0,1,0,0,0,1);
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std::cout<<"here A goes\n"<<A<<"\n";
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int res=cv::optim::solveLP(A,B,z);
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printf("res=%d\n",res);
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printf("scalar %g\n",z.dot(A));
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std::cout<<"here z goes\n"<<z<<"\n";
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ASSERT_EQ(z.dot(A),1);
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//ASSERT_EQ(res,1);
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#endif
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}
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